Many techniques have been developed to transmit and receive ultrasound energy, to process the received data, and to condition the images for display. Typically, the transducer is composed of several individual elements that independently transmit and receive ultrasound energy. To form diagnostically useful images, the received ultrasound energy is focused into beams by summing weighted contributions from the individual elements at carefully selected (and dynamically adjusted) sample times to compensate for the differences in propagation time from each element to points along the beam. Transmit beams are also formed by controlling the relative time of the transmissions from the individual elements. Conventional ultrasound scanners construct image data for a single frame by transmitting and receiving many such beams in succession. The image sequence presented to the human reader and used for diagnosis is constructed from a series of such frames.
The quality of the image sequences produced by conventional ultrasound scanners has several limitations. For example, the frame rate, or temporal resolution, is limited by the propagation time of the ultrasound beams and the fact that constructing a frame of image data requires many sequential beam transmissions. To produce useful image data at acceptable frame rates, ultrasound scanners must process data received from many independent channels (transducer elements) at very high sample rates (e.g., 64 channels at 24 MHz sample rate). This imposes stringent data throughput and computational requirements that are difficult to satisfy. Conventional ultrasound scanners typically address these requirements by incorporating dedicated and highly specialized hardware (e.g., custom designed analog circuitry or digital ASICs) to beamform and to process the resulting data. Because the hardware is so specialized, the functions of these systems are fairly rigidly defined and not easily reconfigurable. Also, once combined by the hardware to form beam data, the original element data are lost (i.e., hardware beamforming is an irreversible process) and are not available for additional processing. For example, if it is desired to form more than one beam from a set of element data by applying different sets of weights and delays (e.g., to increase frame rates), multiple hardware beamformers are required, adding to system complexity, cost, and power consumption.
Another drawback of typical hardware-based beamforming is that it is difficult to use the beamformed data for more than one image modality. The consequence of this is that frame-rates are often dramatically reduced when two or more parametric image sequences (e.g., reflectivity and color velocity) are simultaneously displayed. New parametric image techniques that would expand the diagnostic utility of ultrasound are difficult to achieve because of the rigidity of typical hardware-based beamforming systems.
Spatial resolution is limited by the fact that each transmit beam is typically well focused at only one point (at most) because the relative timing of the respective element transmissions is fixed for each transmission event. Image quality is also limited by the fact that the display coordinates typically do not match the locations where the beams are sampled (e.g., the data may be acquired in polar coordinates, but the image pixels on a display are typically arranged in a rectangular grid). Image formation then requires an interpolation process that is an approximation, resulting in a loss of information.
In addition to the foregoing problems, the information contained in the image data from many conventional scanners is limited to a single two-dimensional plane that has a fixed orientation with respect to the transducer. Methods and apparatuses, typically referred to as 3D/4D systems, have been introduced to address this limitation. These systems interrogate an anatomical volume of interest with ultrasound and reconstruct the received data for the entire volume. A variety of methods may then be used to construct images and image sequences for the entire volume or for desired two-dimensional slices through it. Frame rates and/or spatial resolution are sacrificed, however, because data for the entire volume must be acquired and processed.